Field of the Invention
The present invention relates in general to computer network communications. In one aspect, the present invention relates to an apparatus, system and method for selectively deploying web page content.
Description of the Related Art
The largest and best-known computer network in the world is the Internet which includes millions of computers that are used for commercial, academic, entertainment and government endeavors. With the advent of graphics-based Web browsers (such as Mosaic, Netscape Navigator, and Internet Explorer), the World Wide Web enabled users to access the exponentially growing content provided on the Internet. FIG. 1 illustrates a conventional example of a communication session over the Internet whereby a user at a client computer 110 sends a request for a web page over the Internet 120 that is serviced and returned from a web server 130. When a user 110 begins a communication session over the Internet 120, the user can request data files from an Internet-connected computer called a file server or website server 130. The website server 130 accesses a web page content generator program 142 (shown illustratively as being stored in a database or memory 140) that provides data files, typically in website page format, that are requested by the user. The web pages are typically written in a type of programming code called Hypertext Mark-up Language (HTML) or Extensible Markup Language (XML), and can be viewed or displayed through a browser containing a graphical user interface (GUI) program.
The design, assembly, creation and distribution of web page content 142 or other Internet-based functionality at the web server site 130 can be a complex and expensive undertaking, requiring significant time and expense to assemble a fully functional experience at the server 130. If it is ever necessary or desired that the web page content 142 or other Internet-based functionality at the web server site 130 be enhanced, modified, extended or otherwise altered, the code or architecture at the server 130 must be modified to include the additional content 144 as an alteration to the code or architecture at the server 130. This process is often time-consuming and expensive.
The difficulty of making changes to the code or architecture at the web server 130 also makes it difficult to demonstrate or test new features or functionality for the server 130. For example, the customer who owns a web server 130 may be reluctant to investigate or test new web page features for the web site 130 where the cost, time, risk and effort required to modify the code at the server 130 are significant in comparison to the perceived advantages of the new web page features. An additional challenge is the cost, time, risk and effort required to remove the new feature from the code on the server 130 if the feature demonstration or test is not successful.
To demonstrate the challenges presented by modifying a web site to include additional content 144, consider the example of adding recommendation content or functionality to a retail website hosted at server 130. In this example, the retail website server 130 was initially designed to include a web page content generator 142 which receives order information from the user/client computer 110 and assembles a web page which lists the purchased items contained in a cart for the user. If it is desired to add a purchase recommendation function to the web server 130, this additional functionality can be difficult to add because of the complexity of the required programming and data processing requirements. In particular, now that modern computers can assemble, record and analyze enormous amounts of data, historical transaction data can be collected and analyzed using data mining techniques to generate purchase recommendations for the user to consider based on discovering association relationships in a database by identifying frequently occurring patterns in the database. These association relationships or rules may be applied to extract useful information from large databases in a variety of fields, including selective marketing, market analysis and management applications (such as target marketing, customer relation management, market basket analysis, cross selling, market segmentation), risk analysis and management applications (such as forecasting, customer retention, improved underwriting, quality control, competitive analysis), fraud detection and management applications and other applications (such as text mining (news group, email, documents), stream data mining, web mining, DNA data analysis, etc.). Association rules have been applied to model and emulate consumer purchasing activities by describing how often items are purchased together. Typically, a rule consists of two conditions (e.g., antecedent and consequent) and is denoted as AC where A is the antecedent and C is the consequent. For example, an association rule, “laptopspeaker (80%),” states that four out of five customers that bought a laptop computer also bought speakers.
The difficulty of making recommendations increases as the number and complexity of mined association rules increases, which in turn is caused by an increase in the number of services and/or products, where each service or product may itself comprise a number of constituent services and products. The complexity of recommending a suitable configuration grows further with the number of constituent parts, the external needs of the customer, and the internal needs of the parts when considered as a whole. As will be appreciated, the code required to implement such a recommendation functionality can also be extremely complex so that there can be substantial time and expense required to add such functionality to the web server 130 as additional content 144.
As seen from the conventional approaches, a need exists for methods and/or apparatuses for improving the deployment of web page enhancements that can be quickly and easily integrated with existing web page server systems. There is also a need for improved use, deployment, demonstration and/or testing of data mining techniques to generate purchase recommendations for the end user while minimizing the need to change the coding or architecture at the web server site. There is also a need to seamlessly generate highly granular frequent sets and recommendations for use in an existing web server system without requiring coding changes at the web server. Further limitations and disadvantages of conventional systems will become apparent to one of skill in the art after reviewing the remainder of the present application with reference to the drawings and detailed description which follow.